ArticlePNAS nexus2022
Ranking the information content of distance measures.
Article in PNAS nexus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Temporal rich-club phenomenon and its formation mechanisms in international trade: Evidence from new energy minerals.iScience · 2026Article
- Functional Connectivity Between Human Motor and Somatosensory Areas During a Multifinger Tapping Task: A Proof-of-Concept Study.NeuroSci · 2026Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems.PNAS nexus · 2025Article
- Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.Nature communications · 2025Article
- Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction.Scientific reports · 2024Article
- Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context.Scientific reports · 2024Article
- Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease.Frontiers in neurology · 2023Article
- DADApy: Distance-based analysis of data-manifolds in Python.Patterns (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Finding a small set of features that still retains sufficient information about the dataset is important for the successful application of many statistical learning approaches. We introduce a statistical test that can assess the relative information retained when using 2 different distance measures, and determine if they are equivalent, independent, or if one is more informative than the other. This ranking can in turn be used to identify the most informative distance measure and, therefore, the most informative set of features, out of a pool of candidates. To illustrate the general applicability of our approach, we show that it reproduces the known importance ranking of policy variables for Covid-19 control, and also identifies compact yet informative descriptors for atomic structures. We further provide initial evidence that the information asymmetry measured by the proposed test can be used to infer relationships of causality between the features of a dataset. The method is general and should be applicable to many branches of science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.